In [1]:
"""Template for showing the results of the last experiment in MLFlow."""

import logging
import numpy as np
import helpsk as hlp
import pandas as pd
import plotly_express as px
from helpsk.utility import read_pickle, Timer
from helpsk.sklearn_eval import MLExperimentResults

from source.service.model_registry import ModelRegistry

%cd /code

from source.config import config  # noqa: E402
logging.config.fileConfig(
    "source/config/logging_to_file.conf",
    defaults={'logfilename': 'output/log.log'},
    disable_existing_loggers=False,
)
/usr/local/lib/python3.11/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]
/code

Get Latest Experiment Run from MLFlow¶

In [2]:
registry = ModelRegistry(tracking_uri=config.experiment_server_url())
experiment = registry.get_experiment_by_name(exp_name=config.experiment_name())
logging.info(f"Experiment id: {experiment.last_run.exp_id}")
logging.info(f"Experiment name: {experiment.last_run.exp_name}")
logging.info(f"Run id: {experiment.last_run.run_id}")
logging.info(f"Metric(s): {experiment.last_run.metrics}")
2023-11-24 21:00:13 - INFO     | Experiment id: 1
2023-11-24 21:00:13 - INFO     | Experiment name: credit
2023-11-24 21:00:13 - INFO     | Run id: 7244e48d74144410aa67215f5b59ef38
2023-11-24 21:00:13 - INFO     | Metric(s): {'roc_auc': 0.762767489489011}

Last Run vs Production¶

What is the metric/performance from the model associated with the last run?

In [3]:
logging.info(f"last run metrics: {experiment.last_run.metrics}")
2023-11-24 21:00:13 - INFO     | last run metrics: {'roc_auc': 0.762767489489011}

What is the metric/performance of the model in production?

In [4]:
production_run = registry.get_production_run(model_name=config.model_name())
logging.info(f"production run metrics: {production_run.metrics}")
2023-11-24 21:00:13 - INFO     | production run metrics: {'roc_auc': 0.753377535324465}

Last Run¶

In [5]:
# underlying mlflow object
experiment.last_run.mlflow_entity
Out[5]:
<Run: data=<RunData: metrics={'roc_auc': 0.762767489489011}, params={'model__criterion': 'entropy',
 'model__max_depth': '70',
 'model__max_features': '0.1142268477118407',
 'model__max_samples': '0.5483119512487002',
 'model__min_samples_leaf': '8',
 'model__min_samples_split': '12',
 'model__n_estimators': '553',
 'prep__numeric__imputer__transformer': 'SimpleImputer()',
 'prep__numeric__pca__transformer': "PCA(n_components='mle')",
 'prep__numeric__scaler__transformer': 'None',
 'prep__savings_status__savings_encoder__transformer': 'SavingsStatusEncoder()'}, tags={'mlflow.log-model.history': '[{"run_id": "7244e48d74144410aa67215f5b59ef38", '
                             '"artifact_path": "model", "utc_time_created": '
                             '"2023-11-24 21:00:08.619307", "flavors": '
                             '{"python_function": {"model_path": "model.pkl", '
                             '"predict_fn": "predict", "loader_module": '
                             '"mlflow.sklearn", "python_version": "3.11.6", '
                             '"env": {"conda": "conda.yaml", "virtualenv": '
                             '"python_env.yaml"}}, "sklearn": '
                             '{"pickled_model": "model.pkl", '
                             '"sklearn_version": "1.3.2", '
                             '"serialization_format": "cloudpickle", "code": '
                             'null}}, "model_uuid": '
                             '"8e654c1d76154dbba04829c84f68f68a", '
                             '"mlflow_version": "2.8.0", "model_size_bytes": '
                             '2595199}]',
 'mlflow.note.content': '2023_11_24_20_59_43',
 'mlflow.runName': '2023_11_24_20_59_43',
 'mlflow.source.git.commit': '81a963fcbc4794b8b7bc6c330fc6b034760eb65d',
 'mlflow.source.name': 'source/entrypoints/cli.py',
 'mlflow.source.type': 'LOCAL',
 'mlflow.user': 'root',
 'type': 'BayesSearchCV'}>, info=<RunInfo: artifact_uri='/code/mlflow-artifact-root/1/7244e48d74144410aa67215f5b59ef38/artifacts', end_time=1700859610232, experiment_id='1', lifecycle_stage='active', run_id='7244e48d74144410aa67215f5b59ef38', run_name='2023_11_24_20_59_43', run_uuid='7244e48d74144410aa67215f5b59ef38', start_time=1700859583332, status='FINISHED', user_id='root'>, inputs=<RunInputs: dataset_inputs=[]>>

Load Training & Test Data Info¶

In [6]:
with Timer("Loading training/test datasets"):
    X_train = experiment.last_run.download_artifact(artifact_name='x_train.pkl', read_from=read_pickle)  # noqa
    X_test = experiment.last_run.download_artifact(artifact_name='x_test.pkl', read_from=read_pickle)  # noqa
    y_train = experiment.last_run.download_artifact(artifact_name='y_train.pkl', read_from=read_pickle)  # noqa
    y_test = experiment.last_run.download_artifact(artifact_name='y_test.pkl', read_from=read_pickle)  # noqa
Timer Started: Loading training/test datasets
Timer Finished (0.01 seconds)
In [7]:
logging.info(f"training X shape: {X_train.shape}")
logging.info(f"training y length: {len(y_train)}")

logging.info(f"test X shape: {X_test.shape}")
logging.info(f"test y length: {len(y_test)}")
2023-11-24 21:00:13 - INFO     | training X shape: (800, 20)
2023-11-24 21:00:13 - INFO     | training y length: 800
2023-11-24 21:00:13 - INFO     | test X shape: (200, 20)
2023-11-24 21:00:13 - INFO     | test y length: 200
In [8]:
np.unique(y_train, return_counts=True)
Out[8]:
(array([0, 1]), array([559, 241]))
In [9]:
train_y_proportion = np.unique(y_train, return_counts=True)[1] \
    / np.sum(np.unique(y_train, return_counts=True)[1])
logging.info(f"balance of y in training: {train_y_proportion}")
2023-11-24 21:00:13 - INFO     | balance of y in training: [0.69875 0.30125]
In [10]:
test_y_proportion = np.unique(y_test, return_counts=True)[1] \
    / np.sum(np.unique(y_test, return_counts=True)[1])
logging.info(f"balance of y in test: {test_y_proportion}")
2023-11-24 21:00:13 - INFO     | balance of y in test: [0.705 0.295]

Cross Validation Results¶

Best Scores/Params¶

In [11]:
results = experiment.last_run.download_artifact(
    artifact_name='experiment.yaml',
    read_from=MLExperimentResults.from_yaml_file,
)
logging.info(f"Best Score: {results.best_score}")
logging.info(f"Best Params: {results.best_params}")
2023-11-24 21:00:13 - INFO     | Best Score: 0.762767489489011
2023-11-24 21:00:13 - INFO     | Best Params: {'model': 'RandomForestClassifier()', 'max_features': 0.1142268477118407, 'max_depth': 70, 'n_estimators': 553, 'min_samples_split': 12, 'min_samples_leaf': 8, 'max_samples': 0.5483119512487002, 'criterion': 'entropy', 'imputer': 'SimpleImputer()', 'scaler': 'None', 'pca': "PCA('mle')", 'savings_status_encoder': 'SavingsStatusEncoder()'}
In [12]:
# Best model from each model-type.
data = results.to_formatted_dataframe(return_style=False, include_rank=True)
data["model_rank"] = data.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
data.query('model_rank == 1')
Out[12]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI model C max_features max_depth n_estimators min_samples_split ... colsample_bytree colsample_bylevel reg_alpha reg_lambda num_leaves imputer scaler pca savings_status_encoder model_rank
11 1 0.763 0.726 0.800 RandomForestClassifier() NaN 0.114227 70.0 553.0 12.0 ... NaN NaN NaN NaN NaN SimpleImputer() None PCA('mle') SavingsStatusEncoder() 1.0
0 2 0.759 0.713 0.805 LogisticRegression() NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN SimpleImputer() StandardScaler() None OneHotEncoder() 1.0
19 3 0.758 0.717 0.800 XGBClassifier() NaN NaN 1.0 896.0 NaN ... 0.906088 0.825022 0.003169 1.410800 NaN SimpleImputer(strategy='median') None None SavingsStatusEncoder() 1.0
24 5 0.755 0.723 0.787 LGBMClassifier() NaN NaN NaN NaN NaN ... 0.682707 NaN 5.683878 42.573842 50.0 SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder() 1.0
5 9 0.747 0.698 0.796 ExtraTreesClassifier() NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN SimpleImputer() None None OneHotEncoder() 1.0

5 rows × 26 columns

In [13]:
results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=500)
Out[13]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI model C max_features max_depth n_estimators min_samples_split min_samples_leaf max_samples criterion learning_rate min_child_weight subsample colsample_bytree colsample_bylevel reg_alpha reg_lambda num_leaves imputer scaler pca savings_status_encoder
1 0.763 0.726 0.800 RandomForestClassifier() <NA> 0.114 70.000 553.000 12.000 8.000 0.548 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None PCA('mle') SavingsStatusEncoder()
2 0.759 0.713 0.805 LogisticRegression() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
3 0.758 0.717 0.800 XGBClassifier() <NA> <NA> 1.000 896.000 <NA> <NA> <NA> <NA> 0.029 8.000 0.799 0.906 0.825 0.003 1.411 <NA> SimpleImputer(strategy='median') None None SavingsStatusEncoder()
4 0.758 0.708 0.807 RandomForestClassifier() <NA> 0.681 38.000 1,461.000 23.000 10.000 0.553 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None None OneHotEncoder()
5 0.755 0.723 0.787 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.511 0.683 <NA> 5.684 42.574 50.000 SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
6 0.755 0.720 0.789 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.797 0.700 <NA> 6.654 9.475 381.000 SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
7 0.752 0.711 0.792 RandomForestClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
8 0.751 0.682 0.820 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') MinMaxScaler() None OneHotEncoder()
9 0.747 0.698 0.796 ExtraTreesClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
10 0.741 0.691 0.791 RandomForestClassifier() <NA> 0.710 15.000 1,493.000 33.000 27.000 0.914 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None PCA('mle') OneHotEncoder()
11 0.739 0.671 0.807 LogisticRegression() 23.327 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
12 0.738 0.705 0.772 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
13 0.737 0.667 0.808 ExtraTreesClassifier() <NA> 0.030 84.000 1,088.000 24.000 36.000 0.981 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None SavingsStatusEncoder()
14 0.735 0.683 0.787 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.598 0.661 <NA> 12.533 35.084 348.000 SimpleImputer(strategy='most_frequent') None PCA('mle') SavingsStatusEncoder()
15 0.733 0.666 0.800 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') StandardScaler() None OneHotEncoder()
16 0.732 0.666 0.798 ExtraTreesClassifier() <NA> 0.857 30.000 879.000 17.000 28.000 0.563 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None None SavingsStatusEncoder()
17 0.732 0.664 0.799 ExtraTreesClassifier() <NA> 0.672 81.000 1,136.000 34.000 34.000 0.971 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None None OneHotEncoder()
18 0.731 0.681 0.782 XGBClassifier() <NA> <NA> 5.000 1,218.000 <NA> <NA> <NA> <NA> 0.115 2.000 0.545 0.648 0.852 0.123 1.165 <NA> SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
19 0.729 0.668 0.791 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.845 0.453 <NA> 16.166 40.978 351.000 SimpleImputer(strategy='median') None PCA('mle') SavingsStatusEncoder()
20 0.729 0.674 0.783 LogisticRegression() 0.001 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') StandardScaler() PCA('mle') SavingsStatusEncoder()
21 0.728 0.662 0.794 RandomForestClassifier() <NA> 0.740 14.000 1,645.000 5.000 43.000 0.741 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') None PCA('mle') OneHotEncoder()
22 0.721 0.650 0.792 ExtraTreesClassifier() <NA> 0.781 50.000 590.000 35.000 47.000 0.846 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None PCA('mle') SavingsStatusEncoder()
23 0.714 0.698 0.730 XGBClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
24 0.706 0.684 0.728 XGBClassifier() <NA> <NA> 3.000 682.000 <NA> <NA> <NA> <NA> 0.152 2.000 0.698 0.940 0.817 0.009 2.086 <NA> SimpleImputer(strategy='median') None None SavingsStatusEncoder()
25 0.677 0.602 0.752 XGBClassifier() <NA> <NA> 15.000 1,159.000 <NA> <NA> <NA> <NA> 0.032 29.000 0.834 0.520 0.503 0.003 1.839 <NA> SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
In [14]:
results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)
Out[14]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI max_features max_depth n_estimators min_samples_split min_samples_leaf max_samples criterion imputer pca savings_status_encoder
1 0.763 0.726 0.800 0.114 70.000 553.000 12.000 8.000 0.548 entropy SimpleImputer() PCA('mle') SavingsStatusEncoder()
2 0.758 0.708 0.807 0.681 38.000 1,461.000 23.000 10.000 0.553 gini SimpleImputer(strategy='median') None OneHotEncoder()
3 0.752 0.711 0.792 <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None OneHotEncoder()
4 0.741 0.691 0.791 0.710 15.000 1,493.000 33.000 27.000 0.914 gini SimpleImputer() PCA('mle') OneHotEncoder()
5 0.728 0.662 0.794 0.740 14.000 1,645.000 5.000 43.000 0.741 entropy SimpleImputer(strategy='most_frequent') PCA('mle') OneHotEncoder()
In [15]:
results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)
Out[15]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI C imputer scaler pca savings_status_encoder
1 0.759 0.713 0.805 <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
2 0.751 0.682 0.820 0.000 SimpleImputer(strategy='most_frequent') MinMaxScaler() None OneHotEncoder()
3 0.739 0.671 0.807 23.327 SimpleImputer() StandardScaler() None OneHotEncoder()
4 0.733 0.666 0.800 0.000 SimpleImputer(strategy='most_frequent') StandardScaler() None OneHotEncoder()
5 0.729 0.674 0.783 0.001 SimpleImputer(strategy='median') StandardScaler() PCA('mle') SavingsStatusEncoder()

BayesSearchCV Performance Over Time¶

In [16]:
results.plot_performance_across_trials(facet_by='model').show()
In [17]:
results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

Variable Performance Over Time¶

In [18]:
results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

Scatter Matrix¶

In [19]:
# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

Variable Performance - Numeric¶

In [20]:
results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)
In [21]:
results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

Variable Performance - Non-Numeric¶

In [22]:
results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()

In [23]:
results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='savings_status_encoder',
)

In [24]:
# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )
In [25]:
# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

Last Run - Test Set Performance¶

In [26]:
last_model = experiment.last_run.download_artifact(
    artifact_name='model/model.pkl',
    read_from=read_pickle,
)
print(type(last_model.model))
<class 'sklearn.pipeline.Pipeline'>
In [27]:
last_model
Out[27]:
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'exist...
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=<source.library.pipeline.SavingsStatusEncoder object at 0xffff0c971950>))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(criterion='entropy',
                                                                  max_depth=70,
                                                                  max_features=0.1142268477118407,
                                                                  max_samples=0.5483119512487002,
                                                                  min_samples_leaf=8,
                                                                  min_samples_split=12,
                                                                  n_estimators=553,
                                                                  random_state=42))]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'exist...
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=<source.library.pipeline.SavingsStatusEncoder object at 0xffff0c971950>))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(criterion='entropy',
                                                                  max_depth=70,
                                                                  max_features=0.1142268477118407,
                                                                  max_samples=0.5483119512487002,
                                                                  min_samples_leaf=8,
                                                                  min_samples_split=12,
                                                                  n_estimators=553,
                                                                  random_state=42))]))
Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer())),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credits',
                                                   'num_dependen...
                                                  Pipeline(steps=[('savings_encoder',
                                                                   TransformerChooser(transformer=<source.library.pipeline.SavingsStatusEncoder object at 0xffff0c971950>))]),
                                                  ['savings_status'])])),
                ('model',
                 RandomForestClassifier(criterion='entropy', max_depth=70,
                                        max_features=0.1142268477118407,
                                        max_samples=0.5483119512487002,
                                        min_samples_leaf=8,
                                        min_samples_split=12, n_estimators=553,
                                        random_state=42))])
ColumnTransformer(transformers=[('numeric',
                                 Pipeline(steps=[('imputer',
                                                  TransformerChooser(transformer=SimpleImputer())),
                                                 ('scaler',
                                                  TransformerChooser()),
                                                 ('pca',
                                                  TransformerChooser(transformer=PCA(n_components='mle')))]),
                                 ['duration', 'credit_amount',
                                  'installment_commitment', 'residence_since',
                                  'age', 'existing_credits',
                                  'num_dependents']),
                                ('non_numeric',
                                 Pip...
                                 ['checking_status', 'credit_history',
                                  'purpose', 'employment', 'personal_status',
                                  'other_parties', 'property_magnitude',
                                  'other_payment_plans', 'housing', 'job',
                                  'own_telephone', 'foreign_worker']),
                                ('savings_status',
                                 Pipeline(steps=[('savings_encoder',
                                                  TransformerChooser(transformer=<source.library.pipeline.SavingsStatusEncoder object at 0xffff0c971950>))]),
                                 ['savings_status'])])
['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits', 'num_dependents']
TransformerChooser(transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerChooser()
TransformerChooser(transformer=PCA(n_components='mle'))
PCA(n_components='mle')
PCA(n_components='mle')
['checking_status', 'credit_history', 'purpose', 'employment', 'personal_status', 'other_parties', 'property_magnitude', 'other_payment_plans', 'housing', 'job', 'own_telephone', 'foreign_worker']
OneHotEncoder(handle_unknown='ignore')
['savings_status']
TransformerChooser(transformer=<source.library.pipeline.SavingsStatusEncoder object at 0xffff0c971950>)
RandomForestClassifier(criterion='entropy', max_depth=70,
                       max_features=0.1142268477118407,
                       max_samples=0.5483119512487002, min_samples_leaf=8,
                       min_samples_split=12, n_estimators=553, random_state=42)
In [28]:
test_predictions = last_model.predict(X_test)
test_predictions[0:10]
Out[28]:
array([0.38364602, 0.42461214, 0.50768729, 0.37617143, 0.2083136 ,
       0.33349066, 0.1781312 , 0.4180775 , 0.21162107, 0.25179165])
In [29]:
evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37,
)
In [30]:
evaluator.plot_actual_vs_predict_histogram()
In [31]:
evaluator.plot_confusion_matrix()
No description has been provided for this image
In [32]:
evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)
Out[32]:
  Score Dummy (prior) Dummy (constant) Explanation
AUC 0.782 0.500 0.500 Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate 0.576 0.000 1.000 57.6% of positive instances were correctly identified.; i.e. 34 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate 0.844 1.000 0.000 84.4% of negative instances were correctly identified.; i.e. 119 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate 0.156 0.000 1.000 15.6% of negative instances were incorrectly identified as positive; i.e. 22 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate 0.424 1.000 0.000 42.4% of positive instances were incorrectly identified as negative; i.e. 25 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value 0.607 0.000 0.295 When the model claims an instance is positive, it is correct 60.7% of the time; i.e. out of the 56 times the model predicted "Positive Class", it was correct 34 times; a.k.a precision
Negative Predictive Value 0.826 0.705 0.000 When the model claims an instance is negative, it is correct 82.6% of the time; i.e. out of the 144 times the model predicted "Negative Class", it was correct 119 times
F1 Score 0.591 0.000 0.456 The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC 0.620 0.295 0.295 Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy 0.765 0.705 0.295 76.5% of instances were correctly identified
Error Rate 0.235 0.295 0.705 23.5% of instances were incorrectly identified
% Positive 0.295 0.295 0.295 29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations 200 200 200 There are 200 total observations; i.e. sample size
In [33]:
evaluator.plot_roc_auc_curve().show()
In [34]:
evaluator.plot_precision_recall_auc_curve().show()
In [35]:
evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()
In [36]:
evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()
In [37]:
evaluator.calculate_lift_gain(return_style=True)
/usr/local/lib/python3.11/site-packages/helpsk/sklearn_eval.py:2480: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

Out[37]:
  Gain Lift
Percentile    
5 0.15 3.05
10 0.20 2.03
15 0.31 2.03
20 0.44 2.20
25 0.53 2.10
30 0.59 1.98
35 0.66 1.89
40 0.71 1.78
45 0.76 1.69
50 0.80 1.59
55 0.80 1.45
60 0.81 1.36
65 0.92 1.41
70 0.93 1.33
75 0.95 1.27
80 0.95 1.19
85 0.97 1.14
90 1.00 1.11
95 1.00 1.05
100 1.00 1.00

Production Model - Test Set Performance¶

In [38]:
production_model = production_run.download_artifact(
    artifact_name='model/model.pkl',
    read_from=read_pickle,
)
print(type(production_model.model))
<class 'sklearn.pipeline.Pipeline'>
In [39]:
production_model
Out[39]:
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser())]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'existing_credits',
                                                                             'num_dependents']),
                                                                           ('n...
                                                                             'employment',
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker']),
                                                                           ('savings_status',
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(n_estimators=500,
                                                                  random_state=42))]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser())]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'existing_credits',
                                                                             'num_dependents']),
                                                                           ('n...
                                                                             'employment',
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker']),
                                                                           ('savings_status',
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(n_estimators=500,
                                                                  random_state=42))]))
Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer())),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser())]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credits',
                                                   'num_dependents']),
                                                 ('non_numeric',
                                                  Pipeline(steps...
                                                   'employment',
                                                   'personal_status',
                                                   'other_parties',
                                                   'property_magnitude',
                                                   'other_payment_plans',
                                                   'housing', 'job',
                                                   'own_telephone',
                                                   'foreign_worker']),
                                                 ('savings_status',
                                                  Pipeline(steps=[('savings_encoder',
                                                                   TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                  ['savings_status'])])),
                ('model',
                 RandomForestClassifier(n_estimators=500, random_state=42))])
ColumnTransformer(transformers=[('numeric',
                                 Pipeline(steps=[('imputer',
                                                  TransformerChooser(transformer=SimpleImputer())),
                                                 ('scaler',
                                                  TransformerChooser()),
                                                 ('pca',
                                                  TransformerChooser())]),
                                 ['duration', 'credit_amount',
                                  'installment_commitment', 'residence_since',
                                  'age', 'existing_credits',
                                  'num_dependents']),
                                ('non_numeric',
                                 Pipeline(steps=[('encoder',
                                                  OneHotEncod...n='ignore'))]),
                                 ['checking_status', 'credit_history',
                                  'purpose', 'employment', 'personal_status',
                                  'other_parties', 'property_magnitude',
                                  'other_payment_plans', 'housing', 'job',
                                  'own_telephone', 'foreign_worker']),
                                ('savings_status',
                                 Pipeline(steps=[('savings_encoder',
                                                  TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                 ['savings_status'])])
['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits', 'num_dependents']
TransformerChooser(transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerChooser()
TransformerChooser()
['checking_status', 'credit_history', 'purpose', 'employment', 'personal_status', 'other_parties', 'property_magnitude', 'other_payment_plans', 'housing', 'job', 'own_telephone', 'foreign_worker']
OneHotEncoder(handle_unknown='ignore')
['savings_status']
TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore'))
OneHotEncoder(handle_unknown='ignore')
OneHotEncoder(handle_unknown='ignore')
RandomForestClassifier(n_estimators=500, random_state=42)
In [40]:
test_predictions = production_model.predict(X_test)
test_predictions[0:10]
Out[40]:
array([0.408, 0.522, 0.678, 0.404, 0.088, 0.454, 0.092, 0.492, 0.176,
       0.232])
In [41]:
evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37,
)
In [42]:
evaluator.plot_actual_vs_predict_histogram()
In [43]:
evaluator.plot_confusion_matrix()
No description has been provided for this image
In [44]:
evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)
Out[44]:
  Score Dummy (prior) Dummy (constant) Explanation
AUC 0.815 0.500 0.500 Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate 0.729 0.000 1.000 72.9% of positive instances were correctly identified.; i.e. 43 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate 0.801 1.000 0.000 80.1% of negative instances were correctly identified.; i.e. 113 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate 0.199 0.000 1.000 19.9% of negative instances were incorrectly identified as positive; i.e. 28 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate 0.271 1.000 0.000 27.1% of positive instances were incorrectly identified as negative; i.e. 16 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value 0.606 0.000 0.295 When the model claims an instance is positive, it is correct 60.6% of the time; i.e. out of the 71 times the model predicted "Positive Class", it was correct 43 times; a.k.a precision
Negative Predictive Value 0.876 0.705 0.000 When the model claims an instance is negative, it is correct 87.6% of the time; i.e. out of the 129 times the model predicted "Negative Class", it was correct 113 times
F1 Score 0.662 0.000 0.456 The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC 0.660 0.295 0.295 Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy 0.780 0.705 0.295 78.0% of instances were correctly identified
Error Rate 0.220 0.295 0.705 22.0% of instances were incorrectly identified
% Positive 0.295 0.295 0.295 29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations 200 200 200 There are 200 total observations; i.e. sample size
In [45]:
evaluator.plot_roc_auc_curve().show()
In [46]:
evaluator.plot_precision_recall_auc_curve().show()
In [47]:
evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()
In [48]:
evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()
In [49]:
evaluator.calculate_lift_gain(return_style=True)
/usr/local/lib/python3.11/site-packages/helpsk/sklearn_eval.py:2480: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

Out[49]:
  Gain Lift
Percentile    
5 0.14 2.71
10 0.22 2.20
15 0.36 2.37
20 0.51 2.54
25 0.54 2.17
30 0.68 2.26
35 0.73 2.08
40 0.76 1.91
45 0.76 1.69
50 0.81 1.63
55 0.85 1.54
60 0.85 1.41
65 0.88 1.36
70 0.90 1.28
75 0.95 1.27
80 0.97 1.21
85 0.98 1.16
90 0.98 1.09
95 1.00 1.05
100 1.00 1.00

Feature Importance¶

In [50]:
try:
    importances = production_model.model['model'].feature_importances_
    feature_names = [
        x.replace('non_numeric__', '').replace('numeric__', '')
        for x in production_model.model[:-1].get_feature_names_out()
    ]
    feature_importances = sorted(
        zip(feature_names, importances, strict=True),
        key=lambda x: x[1],
        reverse=False,
    )
    fig = px.bar(
        pd.DataFrame(feature_importances, columns=['feature', 'importance']).tail(20),
        y='feature',
        x='importance',
        orientation='h',
        height=700,
        width=800,
        title='Feature Importances of Production Model',
    )
    fig.show()
except:  # noqa
    print("Error calculating feature importances.")